Average Return On Investment Research Articles

Analyzing ranch profitability from varying cow sales and heifer replacement rates for beef cow-calf production using system dynamics

Ranching involves complex systems and management decisions, which play an important role in financial profits and herd dynamics. A simulation model was built to evaluate the dynamics of a cow-calf ranch under various marketing scenarios. The main objective of this project was to evaluate the impact of three different total cow sales scenarios (low=10%, medium=20% and high=30% of the total cowherd) on ranch Net Income (NI) and Return on Investment (ROI). The system dynamics methodology was used to develop the model and data from a ranch over the period 1996 to 2007 was used to calibrate the model and to perform sensitivity analysis. The data analysis indicated the ranch retained as much as 95% of their annual weaned heifer calves (Bos taurus) to be used in their heifer development program because a large portion of the cowherd was marketed every year. Furthermore, management was rewarded based on performance related to NI and this incentive system has caused an increase in the sales rate of older cows as bred cow replacements. The primary research question was whether the same marketing decisions would have been made if rewards were based on ROI compared to NI. Identifying profit leverage points and understanding what the impact in decreasing heifer replacement rate on NI were secondary interests. Profit leverage points were identified by changing selected variables ±10% from the observed values for the 12-year period while all other variables were held constant. The NI was sensitive to cow pregnancy rate, but it was not sensitive to heifer pregnancy rate or decreasing heifer retention rate prior to the development phase. The model predicted that as cow culling rate increased (i.e. from low to high), NI and ROI also increased. Over the simulated timeframe (i.e. 12years), the high cull rate scenario returned the highest average NI (44% and 19% greater than low and medium, respectively), the greatest average net investments (1.19% and 0.66% greater than low and medium, respectively) and the highest average ROI at 20.55% (versus 14.64% and 17.27% for low and medium, respectively). These results suggested the same marketing decisions would have been made regardless of the bonus incentive structure. The SD model built for this project can be used to develop a web-based, distance education flight simulator that cattle producers and academic researchers can use to model specific production scenarios for their own operation or geographic region.

Production of Male Oreochromis niloticus GET Excel Tilapia by Egg Immersion in Methyl Testosterone Hormone

The masculinization of Nile tilapia, Oreochromis niloticus, GET Excel strain was conducted through egg immersion in two concentrations of 17 α-methyltestosterone (500 and 1000µg L -1 ) at different immersion times (24, 48, 72 and 96 h). The effects of hormone concentration (HC) and immersion time (IT) on egg hatching rates, fry growth, survival and sex ratio were evaluated. Immersion time significantly influenced hatching with decreasing hatching rates at increasing IT. Highest hatching rate of 85% occurred after 24h IT regardless of HC. Low hatching rate of 63 to 65% was obtained at 96h IT. Survival rate of post-treated fry in the net enclosure was not significantly different among treatments. Higher growth occurred in 1000µg L -1 HC although not significantly different from the other treatment. Higher proportions of male juveniles were obtained with increasing HC especially at 1000µg L -1 (97-100%) across all IT. The interaction effect of HC x IT demonstrated that HC at 1000µg L -1 induced production of higher percentages of tilapia males. This implies that HC is a primary factor contributing to the high production of male population of O. niloticus. Cost and return analysis showed an inverse proportion of the return on investment (ROI) with immersion time. Highest average ROI was obtained in 500µg L -1 (55.32%) and 1000µg L -1 (55.25%) HC at 24h IT and lowest in 1000µg L -1 (28.50%) and 500µg L -1 (32.42%) HC at 96h IT. This study has identified the HC and IT of eggs that contributed to highest percentage of male production, a vital information in improving efficiency of all-male tilapia juvenile production to support the growing aquaculture of this important fish species.

Monitoring Worksite Clinic Performance Using a Cost-Benefit Tool

The purpose of this study was to explore the usefulness of continuously assessing the return on investment (ROI) of worksite medical clinics as a means of evaluating clinic performance. Visit data from January 1, 2007, to December 31, 2008, were collected from all the on-site clinics operated for the Pepsi Bottling Group. An average system-wide ROI was calculated from the time of each clinic's opening and throughout the study period. A multivariate linear regression model was used to determine the association of average ROI with penetration/utilization rate and plant size. A total of 26 on-site clinics were actively running as of December 2008. The average ROI at the time of start up was 0.4, which increased to 1.2 at approximately 4 months and 1.6 at the end of the first year of operation. Overall, it seems that the cost of operating a clinic becomes equal to the cost of similar care purchased in the community (ROI = 1) at approximately 3 months after a clinic's opening and flattens out at the end of the first year. The magnitude of the ROI was closely related to the number of visits (a function of the penetration/utilization rate) and the size of the plant population served. Serial monitoring of ROIs is a useful metric in assessing on-site clinic performance and quantifying the effect of new initiatives aimed at increasing a clinic's cost effectiveness.

A Follow-Up Reflection on Software Process Improvement ROI

Our discipline must shift toward value-based software engineering, because we're obliged to prove our contributions to the financial bottom line. In the May/June 2004 IEEE Software special issue on return on investment (ROI), the author presented measurement results for the ROI of software process improvement (SPI). This article made three main contributions. First, provided a detailed overview of publications containing real-life measurement results from practical applications of SPI, in which the author measured the ROI. My study included 20 cases, with an average ROI of 7 and a median of 6.6. This indicates that SPI's net profit seems to be approximately US$7 for every dollar invested. However, I found no published cases in which SPI investments resulted in a measurable loss; furthermore, the ROI bandwidth was large (between 1.5 and 19). This indicates that the actual ROI of an SPI investment seems hard to really guarantee up front. Second, I showed that benefits are just as easy to quantify as costs. Cost measurements are always based on an agreement about how to measure and quantify costs. Such an agreement can also serve as the basis for measuring benefits. My article contained data from two real-life projects that had made such cost and benefit measurements and calculated ROI. Finally, I concluded that expressing value is crucial. Software engineering and its improvement are often major investments for organizations. Investments must be profitable. Because different people in different roles share one generic term for value-money, I recommended expressing any software engineering effort and its benefits in financial terms.

Do Employee Health Management Programs Work?

Current peer review literature clearly documents the economic return and Return-on-Investment (ROI) for employee health management (EHM) programs. These EHM programs are defined as: health promotion, self-care, disease management, and case management programs. The evaluation literature for the sub-set of health promotion and disease management programs is examined in this article for specific evidence of the level of economic return in medical benefit cost reduction or avoidance. The article identifies the methodological challenges associated with determination of economic return for EHM programs and summarizes the findings from 23 articles that included 120 peer review study results. The article identifies the average ROI and percent health plan cost impact to be expected for both types of EHM programs, the expected time period for its occurrence, and caveats related to its measurement.

Implementation and Outcomes of Commercial Disease Management Programs in the United States: The Disease Management Outcomes Consolidation Survey

Despite widespread adoption of disease management (DM) programs by US health plans, gaps remain in the evidence for their benefit. The Disease Management Outcomes Consolidation Survey was designed to gather data on DM programs for commercial health plans, to assess program success and DM effectiveness. The questionnaire was mailed to 292 appropriate health plan contacts; 26 plans covering more than 14 million commercial members completed and returned the survey. Respondents reported that DM plays a significant and increasing role in their organizations. Key reasons for adopting DM were improving clinical outcomes, reducing medical costs and utilization, and improving member satisfaction. More respondents were highly satisfied with clinical results than with utilization or cost outcomes of their programs (46%, 17%, and 13%, respectively). Detailed results were analyzed for 57 DM programs with over 230,000 enrollees. Most responding plans offered DM programs for diabetes and asthma, with return on investment (ROI) ranging from 0.16:1 to 4:1. Weighted by number of enrollees per DM program, average ROI was 2.56:1 for asthma (n = 1,136 enrollees) and 1.98:1 for diabetes (n = 25,364). Most (but not all) respondents reported reduced hospital admissions, increasing rates of preventive care, and improved clinical measures. Few respondents provided detailed information about DM programs for other medical conditions, but most that did reported positive outcomes. Lack of standardized methodology was identified as a major barrier to in-house program evaluation. Although low response rate precluded drawing many general conclusions, a clear need emerged for more rigorous evaluation methods and greater standardization of outcomes measurement.

UNDERPLANTING OF YOUNG COCONUTS UNDER ADULT STANDS OF COCONUT PALMS FOR PRODUCTION OF EDIBLE VEGETATIVE PITH OR 'UBOD'

The feasibility and viability of producing edible vegetative coconut pith or 'ubod' from young coconuts planted with double plants per hill under adult coconut bearing palms was studied at the Philippine Coconut Authority - Davao Research Center, Bago Oshiro Davao City. 
 
 The average weight of 'ubod' from double planting scheme was 5.9 kg/palm (range 2.8 -9.4 kg) on the first year of 'ubod' harvest. There was a very slight increase (average of 0.2 kg/palm) in 'ubod' weight on the second year of harvest. The total ubod yield for two years harvesting schedule was 8.9 t/ha.
 
 Under the Davao growing condition, cost and return analysis of 'ubod' production in double plants per hill scheme indicated a total net income of P129,030 per ha in 4 years with a return on investment (ROI) of 180%. While copra production from adult bearing palms realized a total of P 114,675 for 4 years with an average ROI of 242%. Considering copra plus 'ubod' yield from this production scheme, the cumulative net income and ROI obtained were: P 242,389.6 and 203% respectively.
 
 Based on the results of this study with modest fertilizer application of ammonium sulfate plus common salt (NaCl), underplanting of two young coconuts per hill (spaced at two feet away from each other) at 3 x 3 m distance between spaces of bearing coconut palms harvested at three years from field-planting is a profitable and viable production scheme to increase farm productivity. The farm income with this coconut-based production is increased by 200% compared to coconut monocropping.

ETHICAL DRUG INDUSTRY RETURN ON INVESTMENT*

THE ETHICAL DRUG industry has been under continuous study by governmental investigators and academic researchers for some time. The focal point of many of these studies is the industry's relatively high rate of return on investment (ROI). High rates are often cited as an indication, if not proof, of noncompetitive behavior. However, there is little agreement regarding the definition of ROI, the determinants of ROI, and the proper method for making inter-industry ROI comparisons. These issues were examined and a model was developed to study the ethical drug industry's return on investment. Regression techniques were utilized to test the investment and structural characteristics as potential determinants of ROT. The hypothesized association with ROI was supported for the two risk measures and for growth in demand but rejected for the concentration ratio, average company size, and R and D intensity. The three measures of investment characteristics were combined into a cross-sectional, multiple regression equation giving a model of ROI which was used to study the drug industry's rate of return. Data for the industry were introduced into the model to obtain a forecast of rate of return with variance, skewness, and growth held constant. Comparing the forecast with the observed rate showed observed ethical drug industry average ROI to be below the rate that could result given the particular set of investment characteristics exhibited by the drug industry sample. Therefore, the hypothesis was accepted that ethical drug industry average ROI was the same as allmanufacturing industries average ROI after consideration of investment characteristics. There are several possible explanations for why this model can explain statistically the drug industry ROI: First, data were utilized only for firms primarily engaged in the manufacture of ethical drug products rather than data for both ethical and proprietary drug firms. Second, more recent data may reflect changed economic relationships, both within the drug industry and between the drug and other industries. Third, the model formulated included more variables than prior models. Fourth, explicit consideration was made of possible statistical errors in both the regression equation and in the ROI forecast.